部分可观测性下的神经符号动作模型学习

中文摘要 AI 辅助

AI规划研究智能体如何通过执行一系列动作来达到目标。为了正确规划，智能体需要一个动作模型，描述每个动作何时可以执行以及它如何改变世界。手工构建此类模型需要领域专业知识，且可能成本高昂且容易出错。相反，动作模型可以利用现有的神经符号方法从可用数据中学习，但这些方法目前假设可以访问完全可观测图像的完整轨迹。在部分可观测性下，某些图像可能缺失或不能完全反映世界的当前状态，这些方法无法学习动作模型。因此，本文提出了NeSyAM，一种新颖的神经符号建模范式，用于在部分可观测性下学习动作模型。此外，本文提出了一个统一的变分框架，用于从理论上分析现有方法相对于我们提出的方法的局限性。随后，NeSyAM在六个视觉规划领域和三种观测机制下进行了广泛测试，以表明它在部分可观测性下能够一致地恢复真实动作模型的相关部分。

英文摘要

AI planning studies how an agent can reach a goal by executing a sequence of actions. To plan correctly, the agent needs an action model describing when each action can be executed and how it changes the world. Constructing such models by hand requires domain expertise, and can be costly and error-prone. Action models can instead be learned from available data using existing neurosymbolic approaches, but they currently assume access to complete traces of fully observable images . These approaches fail to learn action models under partial observability where some of the images might not be present or are not fully informative of the current state of the world. Hence, this paper proposes NeSyAM, a novel neurosymbolic modeling paradigm for action model learning under partial observability. In addition, the paper presents a unified variational framework for theoretically analysing the limitations of existing methods compared to our proposed approach. NeSyAM is then tested extensively on six visual planning domains and three observation regimes to show it consistently recovers relevant parts of the true action model under partial observability.

发表机构

• KU Leuven（鲁汶大学）

机构由 AI 辅助整理，请以论文原文为准。